# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Keras 2D convolution layer."""


from keras import activations
from keras import constraints
from keras import initializers
from keras import regularizers
from keras.dtensor import utils
from keras.layers.convolutional.base_conv import Conv

# isort: off
from tensorflow.python.util.tf_export import keras_export


@keras_export("keras.layers.Conv2D", "keras.layers.Convolution2D")
class Conv2D(Conv):
    """2D convolution layer (e.g. spatial convolution over images).

    This layer creates a convolution kernel that is convolved
    with the layer input to produce a tensor of
    outputs. If `use_bias` is True,
    a bias vector is created and added to the outputs. Finally, if
    `activation` is not `None`, it is applied to the outputs as well.

    When using this layer as the first layer in a model,
    provide the keyword argument `input_shape`
    (tuple of integers or `None`, does not include the sample axis),
    e.g. `input_shape=(128, 128, 3)` for 128x128 RGB pictures
    in `data_format="channels_last"`. You can use `None` when
    a dimension has variable size.

    Examples:

    >>> # The inputs are 28x28 RGB images with `channels_last` and the batch
    >>> # size is 4.
    >>> input_shape = (4, 28, 28, 3)
    >>> x = tf.random.normal(input_shape)
    >>> y = tf.keras.layers.Conv2D(
    ... 2, 3, activation='relu', input_shape=input_shape[1:])(x)
    >>> print(y.shape)
    (4, 26, 26, 2)

    >>> # With `dilation_rate` as 2.
    >>> input_shape = (4, 28, 28, 3)
    >>> x = tf.random.normal(input_shape)
    >>> y = tf.keras.layers.Conv2D(
    ...     2, 3,
    ...     activation='relu',
    ...     dilation_rate=2,
    ...     input_shape=input_shape[1:])(x)
    >>> print(y.shape)
    (4, 24, 24, 2)

    >>> # With `padding` as "same".
    >>> input_shape = (4, 28, 28, 3)
    >>> x = tf.random.normal(input_shape)
    >>> y = tf.keras.layers.Conv2D(
    ... 2, 3, activation='relu', padding="same", input_shape=input_shape[1:])(x)
    >>> print(y.shape)
    (4, 28, 28, 2)

    >>> # With extended batch shape [4, 7]:
    >>> input_shape = (4, 7, 28, 28, 3)
    >>> x = tf.random.normal(input_shape)
    >>> y = tf.keras.layers.Conv2D(
    ... 2, 3, activation='relu', input_shape=input_shape[2:])(x)
    >>> print(y.shape)
    (4, 7, 26, 26, 2)


    Args:
      filters: Integer, the dimensionality of the output space (i.e. the number
        of output filters in the convolution).
      kernel_size: An integer or tuple/list of 2 integers, specifying the height
        and width of the 2D convolution window. Can be a single integer to
        specify the same value for all spatial dimensions.
      strides: An integer or tuple/list of 2 integers, specifying the strides of
        the convolution along the height and width. Can be a single integer to
        specify the same value for all spatial dimensions. Specifying any stride
        value != 1 is incompatible with specifying any `dilation_rate` value !=
        1.
      padding: one of `"valid"` or `"same"` (case-insensitive).
        `"valid"` means no padding. `"same"` results in padding with zeros
        evenly to the left/right or up/down of the input. When `padding="same"`
        and `strides=1`, the output has the same size as the input.
      data_format: A string, one of `channels_last` (default) or
        `channels_first`.  The ordering of the dimensions in the inputs.
        `channels_last` corresponds to inputs with shape `(batch_size, height,
        width, channels)` while `channels_first` corresponds to inputs with
        shape `(batch_size, channels, height, width)`. It defaults to the
        `image_data_format` value found in your Keras config file at
        `~/.keras/keras.json`. If you never set it, then it will be
        `channels_last`.
      dilation_rate: an integer or tuple/list of 2 integers, specifying the
        dilation rate to use for dilated convolution. Can be a single integer to
        specify the same value for all spatial dimensions. Currently, specifying
        any `dilation_rate` value != 1 is incompatible with specifying any
        stride value != 1.
      groups: A positive integer specifying the number of groups in which the
        input is split along the channel axis. Each group is convolved
        separately with `filters / groups` filters. The output is the
        concatenation of all the `groups` results along the channel axis. Input
        channels and `filters` must both be divisible by `groups`.
      activation: Activation function to use. If you don't specify anything, no
        activation is applied (see `keras.activations`).
      use_bias: Boolean, whether the layer uses a bias vector.
      kernel_initializer: Initializer for the `kernel` weights matrix (see
        `keras.initializers`). Defaults to 'glorot_uniform'.
      bias_initializer: Initializer for the bias vector (see
        `keras.initializers`). Defaults to 'zeros'.
      kernel_regularizer: Regularizer function applied to the `kernel` weights
        matrix (see `keras.regularizers`).
      bias_regularizer: Regularizer function applied to the bias vector (see
        `keras.regularizers`).
      activity_regularizer: Regularizer function applied to the output of the
        layer (its "activation") (see `keras.regularizers`).
      kernel_constraint: Constraint function applied to the kernel matrix (see
        `keras.constraints`).
      bias_constraint: Constraint function applied to the bias vector (see
        `keras.constraints`).

    Input shape:
      4+D tensor with shape: `batch_shape + (channels, rows, cols)` if
        `data_format='channels_first'`
      or 4+D tensor with shape: `batch_shape + (rows, cols, channels)` if
        `data_format='channels_last'`.

    Output shape:
      4+D tensor with shape: `batch_shape + (filters, new_rows, new_cols)` if
      `data_format='channels_first'` or 4+D tensor with shape: `batch_shape +
        (new_rows, new_cols, filters)` if `data_format='channels_last'`.  `rows`
        and `cols` values might have changed due to padding.

    Returns:
      A tensor of rank 4+ representing
      `activation(conv2d(inputs, kernel) + bias)`.

    Raises:
      ValueError: if `padding` is `"causal"`.
      ValueError: when both `strides > 1` and `dilation_rate > 1`.
    """

    @utils.allow_initializer_layout
    def __init__(
        self,
        filters,
        kernel_size,
        strides=(1, 1),
        padding="valid",
        data_format=None,
        dilation_rate=(1, 1),
        groups=1,
        activation=None,
        use_bias=True,
        kernel_initializer="glorot_uniform",
        bias_initializer="zeros",
        kernel_regularizer=None,
        bias_regularizer=None,
        activity_regularizer=None,
        kernel_constraint=None,
        bias_constraint=None,
        **kwargs
    ):
        super().__init__(
            rank=2,
            filters=filters,
            kernel_size=kernel_size,
            strides=strides,
            padding=padding,
            data_format=data_format,
            dilation_rate=dilation_rate,
            groups=groups,
            activation=activations.get(activation),
            use_bias=use_bias,
            kernel_initializer=initializers.get(kernel_initializer),
            bias_initializer=initializers.get(bias_initializer),
            kernel_regularizer=regularizers.get(kernel_regularizer),
            bias_regularizer=regularizers.get(bias_regularizer),
            activity_regularizer=regularizers.get(activity_regularizer),
            kernel_constraint=constraints.get(kernel_constraint),
            bias_constraint=constraints.get(bias_constraint),
            **kwargs
        )


# Alias

Convolution2D = Conv2D
